Biology subjects

Chung, M.

Publications and source records attributed to Chung, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Multiplexed and reproducible high content screening of live and fixed cells using the Dye Drop method

High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g. CellTiter-Glo(R)). Here we describe a public domain "Dye Drop" method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

systems biology↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗

bioRxiv2021

Cdt1 inhibits CMG helicase in early S phase to separate origin licensing from DNA synthesis

A fundamental concept in eukaryotic DNA replication is the temporal separation of G1 origin licensing from S phase origin firing. Re-replication and genome instability ensue if licensing occurs after DNA synthesis has started. In humans and other vertebrates, the E3 ubiquitin ligase CRL4Cdt2 starts to degrade the licensing factor Cdt1 after origins fire, raising the question of how cells prevent re-replication in early S phase. Here, using quantitative microscopy, we show that Cdt1 inhibits DNA synthesis during an overlap period when cells fire origins while Cdt1 is still present. Cdt1 inhibits DNA synthesis by suppressing CMG helicase progression at replication forks through the MCM-binding domain of Cdt1, and DNA synthesis commences once Cdt1 is degraded. Thus, instead of separating licensing from firing to prevent re-replication in early S phase, cells separate licensing from DNA synthesis through Cdt1-mediated inhibition of CMG helicase after firing. Highlights- Cdt1 is present together with fired origins of replication at the start of S phase - Cdt1 delays DNA synthesis by inhibiting CMG helicase progression after origins fire - Cdt1 inhibits CMG helicase progression through the MCM-binding domain of Cdt1

cell biology↗

bioRxiv2021

Disrupted social memory ensembles in the ventral hippocampus underlie social amnesia in autism-associated Shank3 mutant mice

The ability to remember conspecifics is critical for adaptive cognitive functioning and social communication, and impairments of this ability are hallmarks of autism spectrum disorders (ASDs). Although hippocampal ventral CA1 (vCA1) neurons are known to store social memories, how their activities are coordinated remains unclear. Here we show that vCA1 social memory neurons, characterized by enhanced activity in response to memorized individuals, were preferentially reactivated during sharp-wave ripples (SPW-Rs). Spike sequences of these social replays reflected the temporal orders of neuronal activities within theta cycles during social experiences. In ASD model Shank3 knockout mice, the proportion of social memory neurons was reduced, and neuronal ensemble spike sequences during SPW-Rs were disrupted, which correlated with impaired discriminatory social behavior. These results suggest that SPW-R-mediated sequential reactivation of neuronal ensembles is a canonical mechanism for coordinating hippocampus-dependent social memories and its disruption underlies the pathophysiology of social memory defects associated with ASD.

neuroscience↗

bioRxiv2021

Defective flow-migration coupling causes arteriovenous malformations in hereditary hemorrhagic telangiectasia

BackgroundActivin receptor-like kinase 1 (ACVRL1, hereafter ALK1) is an endothelial transmembrane serine threonine kinase receptor for BMP family ligands that plays a critical role in cardiovascular development and pathology. Loss-of-function mutations in the ALK1 gene cause type 2 hereditary hemorrhagic telangiectasia (HHT), a devastating disorder that leads to arteriovenous malformations (AVMs). Here we show that ALK1 controls endothelial cell polarization against the direction of blood flow and flow-induced endothelial migration from veins through capillaries into arterioles. MethodsUsing Cre lines that recombine in different subsets of arterial, capillary-venous or endothelial tip cells, we showed that capillary-venous Alk1 deletion was sufficient to induce AVM formation in the postnatal retina. ResultsALK1 deletion impaired capillary-venous endothelial cell polarization against the direction of blood flow in vivo and in vitro. Mechanistically, ALK1 deficient cells exhibited increased integrin signaling interaction with VEGFR2, which enhanced downstream YAP/TAZ nuclear translocation. Pharmacological inhibition of integrin or YAP/TAZ signaling rescued flow migration coupling and prevented vascular malformations in Alk1 deficient mice. ConclusionsOur study reveals ALK1 as an essential driver of flow-induced endothelial cell migration and identifies loss of flow-migration coupling as a driver of AVM formation in HHT disease. Integrin-YAP/TAZ signaling blockers are new potential targets to prevent vascular malformations in HHT patients.

cell biology↗

bioRxiv2020

Proteomic profiling of breast cancer cell lines and models

We performed quantitative proteomics on 61 human-derived breast cancer cell lines to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.

cancer biology↗

bioRxiv2020

Latrophilins are essential for endothelial junctional fluid shear stress mechanotransduction

Endothelial cell (EC) responses to fluid shear stress (FSS) are crucial for vascular development, adult physiology and disease. PECAM1 is an important transducer but earlier events remain poorly understood. We therefore investigated heterotrimeric G proteins in FSS sensing. Knockdown (KD) in ECs of single G proteins had little effect but combined depletion of Gi and Gq/11 blocked all known PECAM1-dependent responses. Re-expression of Gi2 and Gq but not Gi1 and Gi3 rescued these effects. Sequence alignment and mutational studies identified that K307 in Gi2 and Gq/11 (Q306 in Gi1/3), determines participation in flow signaling. We developed pull-down assays for measuring G activation and found that this residue, localized to the GPCR interface, determines activation by FSS. We developed a protocol for affinity purification of GPCRs on activated Gs, which identified latrophilins (ADGRLs) as specific upstream interactors for Gi2 and Gq/11. Depletion of latrophilin-2 blocked EC activation of Gi2 and Gq, downstream events in vitro, and flow-dependent vascular morphogenesis in zebrafish embryos. Surprisingly, latrophilin-2 depletion also blocked flow activation of two additional pathways activated at cell-cell junctions, Smad1/5 and Notch1, independently of G proteins. Latrophilins are thus central mediators of junctional shear stress mechanotransduction via G protein-dependent and -independent mechanisms.

molecular biology↗

bioRxiv2019

The Kinase Chemogenomic Set (KCGS): An open science resource for kinase vulnerability identification

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS) is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.

cell biology↗

bioRxiv2019

Combined inhibition of mTOR and PIKKs exploits replicative and checkpoint vulnerabilities to induce death of PI3K-activated triple-negative breast cancer cells

SUMMARY SUMMARY HIGHLIGHTS INTRODUCTION RESULTS DISCUSSION FUNDING AUTHOR CONTRIBUTIONS STAR METHODS Cell lines METHOD DETAILS Western blotting Evaluation of drug sensitivity Immunofluorescence microscopy Primary antibodies and dilutions Single-stranded DNA ssDNA and... Live cell microscopy Extraction of polar metabolites Metabolomics profiling by... Isobolograms and analysis of... Cell cycle model and... QUANTIFICATION AND STATISTICAL... Viable cell counts ...

cancer biology↗

bioRxiv2019

A Bayesian framework for identifying consistent patterns of microbial abundance between body sites

Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.

microbiology↗

bioRxiv2019

A parallel cell-cycle entry pathway with inverted G1 signaling and alternate point of no return

Cell-cycle entry relies on an orderly progression of signaling events. To start, cells first activate the kinase cyclin D-CDK4/6, which leads to eventual inactivation of the retinoblastoma protein Rb. Hours later, cells inactivate APC/CCDH1 and cross the final commitment point. However, many cells with genetically deleted cyclin Ds, which activate and confer specificity to CDK4/6, can compensate and proliferate. Despite its importance in cancer, how this alternate pathway operates and whether wild-type cells use this pathway remain unknown. Here, using single-cell microscopy, we demonstrate that cells with acutely inhibited CDK4/6 enter the cell cycle with slowed and fluctuating cyclin E-CDK2 activity. Surprisingly, in this alternate pathway, the order of APC/CCDH1 and Rb inactivation is inverted in both cell lines and wild-type mice. Finally, we show that as a consequence of the signaling inversion, Rb inactivation replaces APC/CCDH1 inactivation as the point of no return. Together, we provide molecular characterization of a parallel cell-cycle entry pathway, and reveal temporal plasticity that underlies the G1 regulatory circuit.

cell biology↗

bioRxiv2019

Multicenter Analysis of Dosing Protocols for Sotalol Initiation

Sotalol is a Vaughan-Williams Class III antiarrhythmic medication that is commonly used in the management of both atrial and ventricular arrhythmias. Like others in this class, sotalol carries a risk of the potentially lethal arrhythmia torsade de pointes due to its effect of prolonging the QT interval on ECG. For this reason, many centers admit patients for telemetry monitoring during the initial 2-3 days of dosing. However, despite its widespread use, little information is available about the dosing protocols used during this initiation process. In this multicenter investigation, we examine the characteristics of various dose protocols in 213 patients who initiated sotalol over a 4-year period. Of these patients, over 90% were able to successfully complete the dosing regimen (i.e., were discharged on the medication). Significant bradycardia, excessive QT prolongation, and ineffectiveness were the main reasons for failed completion. We found that any dose adjustment was one of the strongest univariate predictors of successful initiation (OR 6.6, 95%CI 1.3 - 32.7, p = 0.021), while initial dose, indication, and resting heart rate or QT interval on baseline ECG did not predict successful initiation. Several predictors of any dose adjustment were identified, and included diabetes, hypertension, presence of pacemaker, heart failure diagnosis, and depressed LV ejection fraction. Using marginal structural models (i.e., inverse probability weighting based on probability of a dose adjustment), we verified that these factors also predicted successful initiation via preventing any dose adjustment, and suggests that consideration of these factors may result in higher likelihood of successful initiation in future investigations. In conclusion, we found that the majority of patients admitted for sotalol initiation are successfully discharged on the medication, often without a single adjustment in the dose. Our findings suggest that several factors predicting lack of dose adjustment could be used clinically to identify patients who could potentially undergo outpatient initiation, although prospective studies are needed to verify this approach.

pharmacology and toxicology↗

bioRxiv2019

Applications of Machine Learning in Decision Analysis for Dose Management for Dofetilide

Initiation of the antiarrhythmic medication dofetilide requires an FDA-mandated 3 days of telemetry monitoring due to heightened risk of toxicity within this time period. Although a recommended dose management algorithm for dofetilide exists, there is a range of real-world approaches to dosing the medication. In this multicenter investigation, we examined the decision process for dose adjustment of dofetilide during the observation period using machine-learning approaches, including supervised, unsupervised, and reinforcement learning applications. Logistic regression approaches identified any dose-adjustment as a strong negative predictor of successful loading (i.e., discharged on dofetilide) of the medication (OR 0.19, 95%CI 0.12 - 0.31, p < 0.001 for discharge on dofetilide), indicating that these adjustments are strong determinants of whether patients "tolerate" the medication. Using multiple supervised approaches, including regularized logistic regression, random forest, boosted gradient decision trees, and neural networks, we were unable to identify any model that predicted dose adjustments better than a naive approach. A reinforcement-learning algorithm, in contrast, predicted which patient characteristics and dosing decisions that resulted in the lowest risk of failure to be discharged on the medication. Future studies could apply this algorithm prospectively to examine improvement over standard approaches.

pharmacology and toxicology↗

bioRxiv2018

3D brain angiogenesis model to reconstitute maturation of functional human blood-brain barrier in vitro

0.Human central nervous system (CNS) vasculature in brain expresses a distinctive barrier phenotype, the blood-brain barrier (BBB), which protects the brain against harmful pathogens. Since the BBB contributes to low success rate in CNS pharmacotherapy by restricting drug transportation, the development of an in vitro human BBB model has been in demand. Previous models were unable to fully represent the complex threedimensional (3D) anatomical structure or specific barrier phenotypes of the matured BBB. In this study, we present a physiological 3D microfluidic model of the human BBB that mimics its developmental process including CNS angiogenesis and subsequent maturation in concert with perivascular cells. We used microfluidic hydrogel patterning to precisely and sequentially load perivascular cells into the model, investigate the role of each cell type on BBB phenotypes. We confirmed the necessity of the tri-culture system (brain endothelium with pericytes and astrocytes) to attain the characteristic BBB vascular morphology such as minimized diameter and maximized junction expression. In addition, endothelial-perivascular cell interaction was also critical in reconstituting p-glycoprotein (p-gp), efflux transporter in our model that works as metabolic barrier of BBB and blocks drug to enter CNS. The 3D hydrogel matrix was tuned with hyaluronic acid (HA) to optimize the interaction between endothelial cells and astrocytes. Our in vitro BBB system mimics CNS angiogenesis and characteristic features of BBB. We expect the model will contribute to deeper understanding of neurodegenerative diseases and cost-efficient development of effective CNS medications.

bioengineering↗

bioRxiv2018

FADU: A Feature Counting Tool for Prokaryotic RNA-Seq Analysis

MotivationThe major algorithms for quantifying transcriptomics data for differential gene expression analysis were designed for analyzing data from human or human-like genomes, specifically those with single gene transcripts and distinct transcriptional boundaries that extend beyond the coding sequence (CDS) as identified through expressed sequence tags (ESTs) or EST-like sequence data. Some eukaryotic genomes and all, or nearly all, bacterial genomes require alternate methods of quantification since they lack annotation of transcriptional boundaries with EST or EST-like data, have overlapping transcriptional boundaries, and/or have polycistronic transcripts.\n\nResultsAn algorithm was developed and tested that better quantifies transcriptomics data for differential gene expression analysis in organisms with overlapping transcriptional units and polycistronic transcripts. Using data from standard libraries originating from Escherichia coli and Ehrlichia chaffeensis, and strand-specific libraries from the Wolbachia endosymbiont wBm, FADU can derive counts for genes that are missed by HTSeq and featurecounts. Using the default parameters with the E. coli data, FADU can detect transcription of 51 more genes than HTSeq in union mode and 21 genes more than featurecounts, with 42 and 18 of these features being <300 bp, respectively. Due to its ability to derive counts for otherwise unrepresented genes without overstating their abundance, we believe FADU to be an improved tool for quantifying transcripts in prokaryotic systems for RNA-Seq analyses.\n\nAvailability and implementationFADU is available at https://github.com/adkinsrs/FADU. FADU was implemented using Python3 and requires the PySAM module (version 0.12.0.1 or later).\n\nContactjdhotopp@som.umaryland.edu

genomics↗

bioRxiv2018

Using Core Genome Alignments to Assign Bacterial Species

With the exponential increase in the number of bacterial taxa with genome sequence data, a new standardized method is needed to assign bacterial species designations using genomic data that is consistent with the classically-obtained taxonomy. This is particularly acute for unculturable obligate intracellular bacteria like those in the Rickettsiales, where classical methods like DNA-DNA hybridization cannot be used to define species. Within the Rickettsiales, species designations have been applied inconsistently, often obfuscating the relationship between organisms and the context for experimental results. In this study, we generated core genome alignments for a wide range of genera with classically defined species, including Arcobacter, Caulobacter, Erwinia, Neisseria, Polaribacter, Ralstonia, Thermus, as well as genera within the Rickettsiales including Rickettsia, Orientia, Ehrlichia, Neoehrlichia, Anaplasma, eorickettsia, and Wolbachia. A core genome alignment sequence identity (CGASI) threshold of 96.8% was found to maximize the prediction of classically-defined species. Using the CGASI cutoff, the Wolbachia genus can be delineated into species that differ from the currently used supergroup designations, while the Rickettsia genus is delineated into nine species, as opposed to the current 27 species. Additionally, we find that core genome alignments cannot be constructed between genomes belonging to different genera, establishing a bacterial genus cutoff that suggests the need to create new genera from the Anaplasma and Neorickettsia. By using core genome alignments to assign taxonomic designations, we aim to provide a high-resolution, robust method for bacterial nomenclature that is aligned with classically-obtained results.

microbiology↗

bioRxiv2018

Targeted enrichment outperforms other enrichment techniques and enables more multi-species RNA-Seq analyses

Enrichment methodologies enable analysis of minor members in multi-species transcriptomic analyses. We compared standard enrichment of bacterial and eukaryotic mRNA to targeted enrichment with Agilent SureSelect (AgSS) capture for Brugia malayi, Aspergillus fumigatus, and the Wolbachia endosymbiont of B. malayi (wBm). Without introducing significant systematic bias, the AgSS quantitatively enriched samples, resulting in more reads mapping to the target organism. The AgSS-enriched libraries consistently had a positive linear correlation with its unenriched counterpart (r2=0.559-0.867). Up to a 2,242-fold enrichment of RNA from the target organism was obtained following a power law (r2=0.90), with the greatest fold enrichment achieved in samples with the largest ratio difference between the major and minor members. While using a single total library for prokaryote and eukaryote in a single sample could be beneficial for samples where RNA is limiting, we observed a decrease in reads mapping to protein coding genes and an increase of multi-mapping reads to rRNAs in AgSS enrichments from eukaryotic total RNA libraries as opposed to eukaryotic poly(A)-enriched libraries. Our results support a recommendation of using Agilent SureSelect targeted enrichment on poly(A)-enriched libraries for eukaryotic captures and total RNA libraries for prokaryotic captures to increase the robustness of multi-species transcriptomic studies.

genomics↗

bioRxiv2018

Transcription factor dynamics reveals a circadian code for fat cell differentiation

Glucocorticoid and other adipogenic hormones are secreted in mammals in circadian oscillations. Loss of this circadian oscillation pattern during stress and disease correlates with increased fat mass and obesity in humans, raising the intriguing question of how hormone secretion dynamics affect the process of adipocyte differentiation. By using live, single-cell imaging of the key adipogenic transcription factors CEBPB and PPARG, endogenously tagged with fluorescent proteins, we show that pulsatile circadian hormone stimuli are rejected by the adipocyte differentiation control system, leading to very low adipocyte differentiation rates. In striking contrast, equally strong persistent signals trigger maximal differentiation. We identify the mechanism of how hormone oscillations are filtered as a combination of slow and fast positive feedback centered on PPARG. Furthermore, we confirm in mice that flattening of daily glucocorticoid oscillations significantly increases the mass of subcutaneous and visceral fat pads. Together, our study provides a molecular mechanism for why stress, Cushings disease, and other conditions for which glucocorticoid secretion loses its pulsatility can lead to obesity. Given the ubiquitous nature of oscillating hormone secretion in mammals, the filtering mechanism we uncovered may represent a general temporal control principle for differentiation.\n\nHIGHLIGHTO_LIWe found that the fraction of differentiated cells is controlled by rhythmic and pulsatile hormone stimulus patterns.\nC_LIO_LITwelve hours is the cutoff point for daily hormone pulse durations below which cells fail to differentiate, arguing for a circadian code for hormone-induced cell differentiation.\nC_LIO_LIIn addition to fast positive feedback such as between PPARG and CEBPA, the adipogenic transcriptional architecture requires added parallel slow positive feedback to mediate temporal filtering of circadian oscillatory inputs\nC_LI

cell biology↗